Study on cerebral blood supply status based on neck bioimpedance
Cerebral blood supply refers to the physiological process whereby the heart continuously and stably delivers oxygen- and nutrient-rich blood to the brain via the vascular system to meet its extremely high metabolic demands. The common carotid artery is one of the most critical blood supply organs connecting the heart and cerebral vasculature, containing abundant information about cerebral blood supply. By assessing the blood flow status in this artery, the degree of cerebral blood supply can be monitored, thereby determining brain neural activity and physiological state. This study investigates the use of neck bioimpedance technology to assess cerebral blood supply status, employing vascular elasticity and blood flow resistance parameters for evaluation. Experimental interventions placed subjects into four states: acute exercise-preceding rest, acute exercise-preceding breath-holding, acute exercise-post-rest, and acute exercise-post-breath-holding. Neck blood flow impedance signals were measured under each state, extracting blood flow resistance and vascular elasticity information. Comparative analysis revealed that the relative change ratios for vascular elasticity parameters were 51.89%, 20.53%, and 33.11% in the pre-exercise breath-hold to post-exercise rest, post-exercise rest to post-exercise breath-hold, and pre-exercise rest to pre-exercise breath-hold states, respectively. The relative change ratio for blood flow resistance parameters was 22.7% in the pre-exercise breath-hold to post-exercise rest state. Statistical analysis revealed significant differences (P < 0.05) in the aforementioned pairwise state transition processes. The experiment validated that carotid bioimpedance monitoring can track cerebral blood supply status, effectively reflecting neural and physiological changes at the whole-brain level. This method holds significant importance for comprehensive monitoring and research into the neurovascular coupling mechanism in the brain.
Authors
- Yunfeng Bai
- Qiang Du
- Xianghua Lu
- Li Ke
Institutions
- Shenyang University of Technology (CN)
Publication Details
- Journal
- IET conference proceedings.
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1049/icp.2025.4227
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.00